GaussDB, and its open source version named openGauss, are Huawei's relational database management systems (RDBMS), featuring a primary disk-based storage engine. This paper presents a new storage engine for GaussDB that is optimized for main memory and many cores. We started from a research prototype which exploits the power of the hardware but is not useful for customers. This paper describes the details of turning this prototype to an industrial storage engine, including integration with GaussDB. Standard benchmarks show that the new engine provides more than 2.5x performance improvement to GaussDB for full TPC-C on Intel's x86 many-cores servers, as well as on Huawei TaiShan servers powered by ARM64-based Kunpeng CPUs.

The telco industry is gradually shifting from using monolithic software packages deployed on custom hardware to using modular virtualized software functions deployed on cloudified data centers using commodity hardware. This transformation is referred to as Network Function Virtualization (NFV). The scalability of the databases (DBs) underlying the virtual network functions is the cornerstone for reaping the benefits from the NFV transformation. This paper presents an industrial experience of applying shared-nothing techniques in order to achieve the scalability of a DB in an NFV setup. The special combination of requirements in NFV DBs are not easily met with conventional execution models. Therefore, we designed a special shared-nothing architecture that is based on cooperative multi-tasking using user-level threads (fibers). We further show that the fiber-based approach outperforms the approach built using conventional multi-threading and meets the variable deployment needs of the NFV transformation. Furthermore, fibers yield a simpler-to-maintain software and enable controlling a trade-off between long-duration computations and real-time requests.

Modern data-centric flows in the telecommunications industry require real time analytical processing over a rapidly changing and large dataset. The traditional approach of separating OLTP and OLAP workloads cannot satisfy this requirement. Instead, a new class of integrated solutions for handling hybrid workloads is needed. This paper presents an industrial use case and a novel architecture that integrates key-value-based event processing and SQL-based analytical processing on the same distributed store while minimizing the total cost of ownership. Our approach combines several well-known techniques such as shared scans, delta processing, a PAX-fashioned storage layout, and an interleaving of scanning and delta merging in a completely new way. Performance experiments show that our system scales out linearly with the number of servers. For instance, our system sustains event streams of 100,000 events per second while simultaneously processing 100 ad-hoc analytical queries per second, using a cluster of 12 commodity servers. In doing so, our system meets all response time goals of our telecommunication customers; that is, 10 milliseconds per event and 100 milliseconds for an ad-hoc analytical query. Moreover, our system beats commercial competitors by a factor of 2.5 in analytical and two orders of magnitude in update performance.